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The agreement between offline orientation gains and reduced real-robot contact-pose failures supports causal future-token distillation as an effective way to improve dynamic manipulation without deploying the world-model teacher.
The FWBC-VLA is proposed, a force-aware framework that bridges task-level VLA action generation and low-level whole-body compensation control for wheeled-legged robots and demonstrates the effectiveness of the FWBC-VLA in contact-rich loco-manipulation.
Key findings demonstrate that the self-attention mechanism of Transformers fundamentally addresses the inherent long-range temporal dependency problem of CNNs, reducing temporal consistency error from approximately 42% for CNNs to roughly 15% for Transformers.
DECOWAM is introduced, a whole-body world-action model that separates camera ego-motion from base and arm actions through dedicated conditional interfaces and shows that embodiment-aware factorization can support parameter-efficient joint visual prediction and whole-body control under moving viewpoints.
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